"BERT-Based GitHub Issue Report Classification" accepted at NLBSE 2022 (co-located with ICSE'22).

Mar 2, 2022. | By: Mohammed Latif Siddiq

Our paper, “BERT-Based GitHub Issue Report Classification”, got accepted for The 1st Intl. Workshop on Natural Language-based Software Engineering (NLBSE’22) co-located with ICSE 2022 in the tool competition. we describe a BERT-based classification technique to automatically label issues as questions, bugs, or enhancements. We evaluate our approach using a dataset containing over 800,000 labeled issues from real open source projects available on GitHub. Our approach classified reported issues with an average F1-score of 0.8571. Our technique outperforms a previous machine learning technique based on FastText.

BibTeX

@inproceedings{siddiq2022bert,
  author = {M. Siddiq and J. C. S. Santos},
  booktitle = {2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)},
  title = {{BERT}-Based GitHub Issue Report Classification},
  year = {2022},
  volume = {1},
  issn = {978-1-4503-9343-0},
  pages = {33-36},
  keywords = {software maintenance;conferences;computer bugs;machine learning;natural language processing;labeling;task analysis},
  doi = {10.1145/3528588.3528660},
  url = {https://doi.ieeecomputersociety.org/},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  month = {may}
}